Innovative Elemental Packaging Design Based on Machine Vision Cognition
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Abstract
Elemental packing plays a fundamental role in understanding the behaviour of materials at the atomic level, influencing their mechanical, thermal, and electrical properties. Optimization plays a crucial role in elemental packing, particularly in the field of materials science and crystallography. The goal of optimization is to find the most energetically favourable arrangement of atoms or molecules within a given structure, aiming to minimize energy and achieve stability. In the context of elemental packing, optimization algorithms are employed to determine the most efficient and stable configurations of atoms in a crystalline lattice. Hence, this paper constructed the integration of Hidden Markov Probabilistic Swarm Optimization (HMPSO) to amplify the capabilities of Machine Cognition systems. At the forefront of this study is the reimagination of elemental packaging design, characterized by aesthetics, ergonomics, and functionality. The infusion of machine vision cognition into these designs not only enhances user experience but also prompts contemplation of ethical considerations surrounding privacy, accessibility, and informed consent. Ethical and social implications are scrutinized comprehensively, acknowledging the profound impact of Machine cognition on individual rights, security, and privacy. The research probes into equitable access to Machine Cognition technologies, ethical data utilization in elemental packing design, identity verification, and surveillance contexts. Central to this multidisciplinary inquiry is the integration of Hidden Markov Probabilistic Swarm Optimization (HMPSO). With swarm intelligence and probabilistic modelling, HMPSO enhances the efficiency, accuracy, and reliability of elemental packing Machine Cognition systems. It addresses the critical challenge of reducing false positives and false negatives in Machine Cognition authentication. The research methodology comprises performance evaluations, ethical analyses, and sociocultural investigations, offering a comprehensive view of the interplay between design innovation, machine vision cognition, ethical awareness, and the application of HMPSO in the elemental packing Machine Cognitions.
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References
Andrews, J. T., Zhao, D., Thong, W., Modas, A., Papakyriakopoulos, O., Nagpal, S., & Xiang, A. (2023). Ethical considerations for collecting human-centric image datasets. arXiv preprint arXiv:2302.03629.
Zhu, H. Y., Hieu, N. Q., Hoang, D. T., Nguyen, D. N., & Lin, C. T. (2023). A Human-Centric Metaverse Enabled by Brain-Computer Interface: A Survey. arXiv preprint arXiv:2309.01848.
Asad, U., Khan, M., Khalid, A., & Lughmani, W. A. (2023). Human-Centric Digital Twins in Industry: A Comprehensive Review of Enabling Technologies and Implementation Strategies. Sensors, 23(8), 3938.
Paullada, A., Raji, I. D., Bender, E. M., Denton, E., & Hanna, A. (2021). Data and its (dis) contents: A survey of dataset development and use in machine learning research. Patterns, 2(11).
Yang, W., Wei, Y., Wei, H., Chen, Y., Huang, G., Li, X., ... & Kang, B. (2023). Survey on Explainable AI: From Approaches, Limitations and Applications Aspects. Human-Centric Intelligent Systems, 1-28.
Górriz, J. M., Álvarez-Illán, I., Álvarez-Marquina, A., Arco, J. E., Atzmueller, M., Ballarini, F., ... & Ferrández-Vicente, J. M. (2023). Computational approaches to Explainable Artificial Intelligence: Advances in theory, applications and trends. Information Fusion, 100, 101945.
Mallick, P. K., Balas, V. E., Bhoi, A. K., & Zobaa, A. F. (2021). Cognitive informatics and soft computing. Springer Singapore.
Nasir, S., Khan, R. A., & Bai, S. (2023). Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond. arXiv preprint arXiv:2309.00064.
Liang, P. P., Lyu, Y., Fan, X., Wu, Z., Cheng, Y., Wu, J., ... & Morency, L. P. (2021). Multibench: Multiscale benchmarks for multimodal representation learning. arXiv preprint arXiv:2107.07502.
Gu, X., Deligianni, F., Han, J., Liu, X., Chen, W., Yang, G. Z., & Lo, B. (2023). Beyond supervised learning for pervasive healthcare. IEEE Reviews in Biomedical Engineering.
Brophy, E. (2022). Deep learning-based signal processing approaches for improved tracking of human health and behaviour with wearable sensors (Doctoral dissertation, Dublin City University).
Hulsen, T. (2023). Explainable Artificial Intelligence (XAI): Concepts and Challenges in Healthcare. AI, 4(3), 652-666.
Selvi, C., Victor, N., Chengoden, R., Bhattacharya, S., Maddikunta, P. K. R., Lee, D., ... & Gadekallu, T. R. (2023). A Comprehensive Analysis of Blockchain Applications for Securing Computer Vision Systems. arXiv preprint arXiv:2307.06659.
Mirzaei, G., & Adeli, H. (2022). Machine learning techniques for diagnosis of alzheimer disease, mild cognitive disorder, and other types of dementia. Biomedical Signal Processing and Control, 72, 103293.
Kline, A., Wang, H., Li, Y., Dennis, S., Hutch, M., Xu, Z., ... & Luo, Y. (2022). Multimodal machine learning in precision health: A scoping review. npj Digital Medicine, 5(1), 171.
Dell’Agnola, F., Jao, P. K., Arza, A., Chavarriaga, R., Millán, J. D. R., Floreano, D., & Atienza, D. (2022). Machine-Learning Based Monitoring of Cognitive Workload in Rescue Missions With Drones. IEEE Journal of Biomedical and Health Informatics, 26(9), 4751-4762.
Rahman, A., Chowdhury, M. E., Khandakar, A., Kiranyaz, S., Zaman, K. S., Reaz, M. B. I., ... & Kadir, M. A. (2021). Multimodal EEG and keystroke dynamics based biometric system using machine learning algorithms. IEEE Access, 9, 94625-94643.
Hilfer-Hadamard Fractional Differential Equations; Existence and Attractivity. (2021). Advances in the Theory of Nonlinear Analysis and Its Application, 5(1), 49-57. https://atnaea.org/index.php/journal/article/view/181
Junaid, M., Ali, S., Eid, F., El-Sappagh, S., & Abuhmed, T. (2023). Explainable machine learning models based on multimodal time-series data for the early detection of Parkinson’s disease. Computer Methods and Programs in Biomedicine, 234, 107495.
Sharma, H., Drukker, L., Papageorghiou, A. T., & Noble, J. A. (2021). Machine learning-based analysis of operator pupillary response to assess cognitive workload in clinical ultrasound imaging. Computers in biology and medicine, 135, 104589.
Lim, J. Z., Mountstephens, J., & Teo, J. (2022). Eye-tracking feature extraction for biometric machine learning. Frontiers in neurorobotics, 15, 796895.
Vishal Chauhan, Shoyab Ali, & Dr. Pramod Sharma. (2022). Numerical Simulation and Design of Improved MPPT for Solar Photovoltaic System for Renewable Energy Applications. International Journal on Recent Technologies in Mechanical and Electrical Engineering, 9(1), 01–07. https://doi.org/10.17762/ijrmee.v9i1.358
Satish, A. B. ., & Rechanna, D. (2022). An Empirical Study on the Impact of HRD Practices on NPOs in Utilizing Foreign Contributions Received. International Journal of New Practices in Management and Engineering, 11(01), 01–14. https://doi.org/10.17762/ijnpme.v11i01.143
Bayoudh, K., Knani, R., Hamdaoui, F., & Mtibaa, A. (2021). A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets. The Visual Computer, 1-32.
Gnacek, M., Broulidakis, J., Mavridou, I., Fatoorechi, M., Seiss, E., Kostoulas, T., ... & Nduka, C. (2022). emteqpro—fully integrated biometric sensing array for non-invasive biomedical research in virtual reality. Frontiers in virtual reality, 3, 781218.
Kinreich, S., McCutcheon, V. V., Aliev, F., Meyers, J. L., Kamarajan, C., Pandey, A. K., ... & Porjesz, B. (2021). Predicting alcohol use disorder remission: a longitudinal multimodal multi-featured machine learning approach. Translational psychiatry, 11(1), 166.
Chen, H., Li, W., Sheng, X., Ye, Q., Zhao, H., Xu, Y., ... & Alzheimer’s Disease Neuroimaging Initiative. (2022). Machine learning based on the multimodal connectome can predict the preclinical stage of Alzheimer’s disease: a preliminary study. European Radiology, 32, 448-459.
Sharma, S., & Mandal, P. K. (2022). A comprehensive report on machine learning-based early detection of alzheimer's disease using multi-modal neuroimaging data. ACM Computing Surveys (CSUR), 55(2), 1-44.
Aguayo, G. A., Zhang, L., Vaillant, M., Ngari, M., Perquin, M., Moran, V., ... & Fagherazzi, G. (2023). Machine learning for predicting neurodegenerative diseases in the general older population: a cohort study. BMC medical research methodology, 23(1), 1-13.
Qiu, S., Zhao, H., Jiang, N., Wang, Z., Liu, L., An, Y., ... & Fortino, G. (2022). Multi-sensor information fusion based on machine learning for real applications in human activity recognition: State-of-the-art and research challenges. Information Fusion, 80, 241-265.
Shams, T. B., Hossain, M. S., Mahmud, M. F., Tehjib, M. S., Hossain, Z., & Pramanik, M. I. (2022). EEG-based Biometric Authentication Using Machine Learning: A Comprehensive Survey. ECTI Transactions on Electrical Engineering, Electronics, and Communications, 20(2), 225-241.
Kaushal, N., Singh, S., & Kumar, J. (2021). Attack Detection Using Deep Learning–Based Multimodal Biometric Authentication System. Cognitive Behavior and Human Computer Interaction Based on Machine Learning Algorithm, 157-165.